Accelerating The Information-Theoretic Approach Of Community Detection Using Distributed And Hybrid Memory Parallel Schemes,
2020
LSU New Orleans
Accelerating The Information-Theoretic Approach Of Community Detection Using Distributed And Hybrid Memory Parallel Schemes, Md Abdul Motaleb Faysal
LSU New Orleans Theses and Dissertations
There are several approaches for discovering communities in a network (graph). Despite being approximating in nature, discovering communities based on the laws of Information Theory has a proven standard of accuracy. The information-theoretic algorithm known as Infomap developed a decade ago for detecting communities, did not foresee the tremendous growth of social networking, multimedia, and massive information boom. To discover communities in massive networks, we have designed a distributed-memory-parallel Infomap in the MPI framework. Our design reaches scalability of over 500 processes capable of processing networks with millions of edges while maintaining quality comparable to the sequential Infomap. We have …
Higher-Order Link Prediction Using Graph Embeddings,
2020
San Jose State University
Higher-Order Link Prediction Using Graph Embeddings, Neeraj Chavan
Master's Projects
Link prediction is an emerging field that predicts if two nodes in a network are likely to be connected or not in the near future. Networks model real-world systems using pairwise interactions of nodes. However, many of these interactions may involve more than two nodes or entities simultaneously. For example, social interactions often occur in groups of people, research collaborations are among more than two authors, and biological networks describe interactions of a group of proteins. An interaction that consists of more than two entities is called a higher-order structure. Predicting the occurrence of such higher-order structures helps us solve …
Rehearsal Scheduling Problem,
2020
San Jose State University
Rehearsal Scheduling Problem, Thuan Bao
Master's Projects
Scheduling is a common task that plays a crucial role in many industries such as manufacturing or servicing. In a competitive environment, effective scheduling is one of the key factors to reduce cost and increase productivity. Therefore, scheduling problems have been studied by many researchers over the past thirty years. Rehearsal scheduling problem (RSP) is similar to the popular resource-constrained project scheduling problem (RCPSP); however, it does not have activity precedence constraints and the resources’ availabilities are not fixed during processing time. RSP can be used to schedule rehearsal in theatre industry or to schedule group scheduling when each member …
Graphical Representation Of Text Semantics,
2020
Kennesaw State University
Graphical Representation Of Text Semantics, Karl Kevin Tiba Fossoh
Master of Science in Computer Science Theses
A text is a set of words conveying a particular semantic based on their order, representation and structure. Those elements can be associated through a different set of interpretations, based on frequency and proportionality. The problem with context is that numbers do not help understand the semantics and fall short to convey the message of the text. The graphical representation of text semantics focuses on the conversion of text to images. Contrarily to word clouds that simply produce frequency mapping of words within the text and topic models that essentially give context to word frequencies and proportionalities, images keep intact …
Csp-Completeness And Its Applications,
2020
Washington University in St. Louis
Csp-Completeness And Its Applications, Alexander Durgin
McKelvey School of Engineering Graduate Student Theses & Dissertations
We build off of previous ideas used to study both reductions between CSPrefutation problems and improper learning and between CSP-refutation problems themselves to expand some hardness results that depend on the assumption that refuting random CSP instances are hard for certain choices of predicates (like k-SAT). First, we are able argue the hardness of the fundamental problem of learning conjunctions in a one-sided PAC-esque learning model that has appeared in several forms over the years. In this model we focus on producing a hypothesis that foremost guarantees a small false-positive rate while minimizing the false-negative rate for such hypotheses. Further, …
Voxel Optimization,
2020
Minnesota State University Moorhead
Voxel Optimization, Scott Bengs
Student Academic Conference
Voxel Optimization This poster presentation covers optimization for voxels. They can be thought of as three dimensional pixels. Vo coming from volume and xel from pixel. Voxels are just values placed in a 3D grid. Voxels have many interesting uses in the medical and scientific field, especially in geology. One use in computer science is storing world information for video games or graphical applications. One very popular example is Minecraft, a game that allows all of the world to be changed, that uses cube shaped voxels. The first topic will be on the naive approach of building a model from …
The Theory Of Cryptography In Bitcoin,
2020
Louisiana Tech University
The Theory Of Cryptography In Bitcoin, Can Hong
Mathematics Senior Capstone Papers
Bitcoin is a well known virtual currency, or cryptocurrency. It was created by a group of people using the name Satoshi Nakamoto in 2008. Currently, many people are utilizing Bitcoin for personal gains and transactions. To keep transactions secure requires techniques from modern cryptography. In this paper, we explain certain aspects of the cryptography of Bitcoin. We are going to discuss two components of the cryptography of Bitcoin—hash functions and signatures. We will describe what the hash function and signature are, give some examples of hash functions, and discuss certain criteria that good hash functions should satisfy.
Predictive Modeling Of Asynchronous Event Sequence Data,
2020
Louisiana State University
Predictive Modeling Of Asynchronous Event Sequence Data, Jin Shang
LSU Doctoral Dissertations
Large volumes of temporal event data, such as online check-ins and electronic records of hospital admissions, are becoming increasingly available in a wide variety of applications including healthcare analytics, smart cities, and social network analysis. Those temporal events are often asynchronous, interdependent, and exhibiting self-exciting properties. For example, in the patient's diagnosis events, the elevated risk exists for a patient that has been recently at risk. Machine learning that leverages event sequence data can improve the prediction accuracy of future events and provide valuable services. For example, in e-commerce and network traffic diagnosis, the analysis of user activities can be …
Generating Acoustic Projections Using 3d Models,
2020
James Madison University
Generating Acoustic Projections Using 3d Models, Jake A. Brazelton
Senior Honors Projects, 2020-current
Raytracing is used in commercial graphics engines most commonly for lighting effects, but it also has many uses when it comes to acoustic simulation. Adopted directly from these computer graphics programs, the formulas presented herein enable the visualization of acoustic intensity levels throughout a 3D space using Python 3 and the OpenGL library. In addition to visualization, they also provide the ability to calculate the reverberation time and critical distance of an enclosed space in relation to its size and material makeup. The described application bundles all of these components together in a Qt5 application that allows users to view …
Applying Imitation And Reinforcement Learning To Sparse Reward Environments,
2020
University of Arkansas, Fayetteville
Applying Imitation And Reinforcement Learning To Sparse Reward Environments, Haven Brown
Computer Science and Computer Engineering Undergraduate Honors Theses
The focus of this project was to shorten the time it takes to train reinforcement learning agents to perform better than humans in a sparse reward environment. Finding a general purpose solution to this problem is essential to creating agents in the future capable of managing large systems or performing a series of tasks before receiving feedback. The goal of this project was to create a transition function between an imitation learning algorithm (also referred to as a behavioral cloning algorithm) and a reinforcement learning algorithm. The goal of this approach was to allow an agent to first learn to …
On The Explanation And Implementation Of Three Open-Source Fully Homomorphic Encryption Libraries,
2020
University of Arkansas, Fayetteville
On The Explanation And Implementation Of Three Open-Source Fully Homomorphic Encryption Libraries, Alycia Carey
Computer Science and Computer Engineering Undergraduate Honors Theses
While fully homomorphic encryption (FHE) is a fairly new realm of cryptography, it has shown to be a promising mode of information protection as it allows arbitrary computations on encrypted data. The development of a practical FHE scheme would enable the development of secure cloud computation over sensitive data, which is a much-needed technology in today's trend of outsourced computation and storage. The first FHE scheme was proposed by Craig Gentry in 2009, and although it was not a practical implementation, his scheme laid the groundwork for many schemes that exist today. One main focus in FHE research is the …
Heuristics For Sparsest Cut Approximations In Network Flow Applications,
2020
Southern Methodist University
Heuristics For Sparsest Cut Approximations In Network Flow Applications, Fernando Vilas
Computer Science and Engineering Theses and Dissertations
The Maximum Concurrent Flow Problem (MCFP) is a polynomially bounded problem that has been used over the years in a variety of applications. Sometimes it is used to attempt to find the Sparsest Cut, an NP-hard problem, and other times to find communities in Social Network Analysis (SNA) in its hierarchical formulation, the HMCFP. Though it is polynomially bounded, the MCFP quickly grows in space utilization, rendering it useful on only small problems. When it was defined, only a few hundred nodes could be solved, where a few decades later, graphs of one to two thousand nodes can still be …
Achieving Causal Fairness In Machine Learning,
2020
University of Arkansas, Fayetteville
Achieving Causal Fairness In Machine Learning, Yongkai Wu
Graduate Theses and Dissertations
Fairness is a social norm and a legal requirement in today's society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974) have been established to prohibit discrimination and enforce fairness on several grounds, such as gender, age, sexual orientation, race, and religion, referred to as sensitive attributes. Nowadays machine learning algorithms are extensively applied to make important decisions in many real-world applications, e.g., employment, admission, and loans. Traditional machine learning algorithms aim to maximize predictive performance, e.g., accuracy. Consequently, certain groups may get unfairly treated when those algorithms are applied for decision-making. Therefore, it is an imperative …
Dependency Mapping Software For Jira, Project Management Tool,
2020
University of Arkansas, Fayetteville
Dependency Mapping Software For Jira, Project Management Tool, Bentley Lager
Computer Science and Computer Engineering Undergraduate Honors Theses
Efficiently managing a software development project is extremely important in industry and is often overlooked by the software developers on a project. Pieces of development work are identified by developers and are then handed off to project managers, who are left to organize this information. Project managers must organize this to set expectations for the client, and ensure the project stays on track and on budget. The main block in this process are dependency chains between tasks. Dependency chains can cause a project to take much longer than anticipated or result in the under utilization of developers on a project. …
Robust Graph Learning From Noisy Data,
2020
Singapore Management University
Robust Graph Learning From Noisy Data, Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu
Research Collection School Of Computing and Information Systems
Learning graphs from data automatically have shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from the real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust principle component analysis (RPCA), where the quality of the graph plays a critical role. The proposed model is able to …
A Matheuristic Algorithm For Solving The Vehicle Routing Problem With Cross-Docking,
2020
Singapore Management University
A Matheuristic Algorithm For Solving The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
Research Collection School Of Computing and Information Systems
This paper studies the integration of the vehicle routing problem with cross-docking, namely VRPCD. The aim is to find a set of routes to deliver single products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic approach that uses the routes of the local optima of an adaptive large neighborhood search (ALNS) as columns in a set-partitioning formulation of the VRPCD is designed. This matheuristic outperforms the state-of-the-art algorithms in solving a subset of …
Shakespeare In The Eighteenth Century: Algorithm For Quotation Identification,
2020
University of Arkansas, Fayetteville
Shakespeare In The Eighteenth Century: Algorithm For Quotation Identification, Marion Pauline Chiariglione
Graduate Theses and Dissertations
Quoting a borrowed excerpt of text within another literary work was infrequently done prior to the beginning of the eighteenth century. However, quoting other texts, particularly Shakespeare, became quite common after that. Our work develops automatic approaches to identify that trend. Initial work focuses on identifying exact and modified sections of texts taken from works of Shakespeare in novels spanning the eighteenth century. We then introduce a novel approach to identifying modified quotes by adapting the Edit Distance metric, which is character based, to a word based approach. This paper offers an introduction to previous uses of this metric within …
Advancing Performance Of Retail Recommendation Systems,
2020
Southern Methodist University
Advancing Performance Of Retail Recommendation Systems, Lisa Leininger, Johnny Gipson, Kito Patterson, Brad Blanchard
SMU Data Science Review
This paper presents two recommendation models, one traditional and one novel, for a retail men's clothing company. J. Hilburn is a custom-fit, menswear clothing company headquartered in Dallas, Texas. J. Hilburn employs stylists across the United States, who engage directly with customers to assist in selecting clothes that fit their size and style. J. Hilburn tasked the authors of this paper to leverage data science techniques to the given data set to provide stylists with more insight into clients’ purchase patterns and increase overall sales. This paper presents two recommendation systems which provide stylists with automatic predictions about possible clothing …
Improving Syntactic Relationships Between Language And Objects,
2020
Southern Methodist University
Improving Syntactic Relationships Between Language And Objects, Benjamin Wilke, Tej Tenmattam, Anand Rajan, Andrew Pollock, Joel Lindsey
SMU Data Science Review
This paper presents the integration of natural language processing and computer vision to improve the syntax of the language generated when describing objects in images. The goal was to not only understand the objects in an image, but the interactions and activities occurring between the objects. We implemented a multi-modal neural network combining convolutional and recurrent neural network architectures to create a model that can maximize the likelihood of word combinations given a training image. The outcome was an image captioning model that leveraged transfer learning techniques for architecture components. Our novelty was to quantify the effectiveness of transfer learning …
Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring,
2020
Kennesaw State University
Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring, Yan Wang
Doctor of Data Science and Analytics Dissertations
In this dissertation, we develop and discuss several loan evaluation methods to guide the investment decisions for peer-to-peer (P2P) lending. In evaluating loans, credit scoring and profit scoring are the two widely utilized approaches. Credit scoring aims at minimizing the risk while profit scoring aims at maximizing the profit. This dissertation addresses the strengths and weaknesses of each scoring method by integrating them in various ways in order to provide the optimal investment suggestions for different investors. Before developing the methods for loan evaluation at the individual level, we applied the state-of-the-art method called the Long Short Term Memory (LSTM) …
